Provision and use of physical rehabilitation services for adults with disabilities in Rwanda: A descriptive study
Bibliographic record
Abstract
Background: Physical rehabilitation interventions address functional deficits caused by impairments that affect someone's performance. Whilst rehabilitation is important, it is assumed that these services are either minimal or nonexistent in low-resource settings. Our data expand on the data from the Situation Assessment of Rehabilitation in the Republic of Rwanda report to describe rehabilitation services and who access them at public and semiprivate facilities (primarily funded by the private sector). Objectives: This article describes the use of the outpatient physical rehabilitation services across nine health facilities, the characteristics of adults attending these health facilities and some of the facilitators and barriers they encounter when attending rehabilitation. Method: Data were collected between September and December 2018 from the heads of departments and adult patients attending outpatient rehabilitation services funded by the government, international nongovernmental organisations or faith-based organisations. Results: Two hundred and thirteen adults were recruited from nine facilities. There is a sixfold difference in the number of rehabilitation personnel between public and semiprivate hospitals in these facilities' catchment areas. However, most participants were recruited at public facilities (186 [87%]), primarily with physical disorders. Patients reported that family support (94%) was the most crucial facilitator for attending rehabilitation, whilst transportation cost (96%) was a significant barrier. Conclusion: Rehabilitation service availability for Rwandan adults with disabilities is limited. Whilst family support helps patients attend rehabilitation, transportation costs remain a significant barrier to people attending rehabilitation. Strategies to address these issues include developing triage protocols, training community health workers and families. Contribution: Data on rehabilitation service provision in Rwanda and most African countries are either non-existent or very limited. These data contain important information regarding the services provided and the people who used them across different health facilities (public versus private) and urban versus rural settings). To improve rehabilitation service provision, we first need to understand the current situation. These data are an important step to better understanding rehabilitation in Rwanda.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".